Tsukatmoto Fuzzy Logic simple example
of 6
Transcript of Tsukatmoto Fuzzy Logic simple example
-
8/19/2019 Tsukatmoto Fuzzy Logic simple example
1/11
FUZZY LOGIC‘TSUKAMOTO
-
8/19/2019 Tsukatmoto Fuzzy Logic simple example
2/11
1. Fuzzyfication
Membentuk variabel input dan variabel output, himpunan fuzzyserta derajat keanggotaannya (fungsi segitiga dan trapesium).
Variabel Kelembaban
-
8/19/2019 Tsukatmoto Fuzzy Logic simple example
3/11
Fungsi Keanggotaan Kelembaban
Soggy x
0 500 ≤ ≤ 0( − 0)/(250 − 0) 0 ≤ ≤ 250
500 −
500 − 250 250 ≤ ≤ 500
1 250
Wet x
0 800 ≤ ≤ 500
( − 500)/(650 − 500) 500 ≤ ≤ 650
800 −
800 − 650 650 ≤ ≤ 800
1 650
Dry x
0 1023 ≤ ≤ 800
( − 800)/(910 − 800) 800 ≤ ≤ 910
1023 −
1023 − 910 910 ≤ ≤ 1023
1 910
-
8/19/2019 Tsukatmoto Fuzzy Logic simple example
4/11
Variabel Suhu
-
8/19/2019 Tsukatmoto Fuzzy Logic simple example
5/11
Fungsi Keanggotaan Suhu
Cold x
0 25 ≤ ≤ 20( − 20)/(22,5 − 20) 20 ≤ ≤ 22,5
25 −
25 − 22,5 22,5 ≤ ≤ 25
1 22,5
Normal x
0 27,5 ≤ ≤ 22,5( − 22,5)/(25 − 22,5) 22,5 ≤ ≤ 25
27,5 −
27,5 − 25 25 ≤ ≤ 27,5
1 25
Warm x
0 30 ≤ ≤ 25( − 25)/(27,5 − 25) 25 ≤ ≤ 27,5
30 −
30 − 27,5 27,5 ≤ ≤ 30
1 27,5
-
8/19/2019 Tsukatmoto Fuzzy Logic simple example
6/11
Variabel Kecepatan Fan
-
8/19/2019 Tsukatmoto Fuzzy Logic simple example
7/11
Fungsi Keanggotaan Kecepatan Fan
Slow z
0 150 ≤ ≤ 100( − 100)/(125 − 100) 100 ≤ ≤ 125
150 −
150 − 125 125 ≤ ≤ 150
1 125
Normal z
0 200 ≤ ≤ 150( − 150)/(175 − 150) 150 ≤ ≤ 175
200 −
200 − 175 175 ≤ ≤ 200
1 175
Fast z
0 250 ≤ ≤ 200( − 200)/(225 − 200) 200 ≤ ≤ 225
250 −
250 − 225 225 ≤ ≤ 250
1 225
-
8/19/2019 Tsukatmoto Fuzzy Logic simple example
8/11
2. Inference Engine (Reasoning)
Pembentukan Rule Based (IF-THEN)
[1] rule 1: JIKA kelembaban soggy AND suhu cold THEN slow
[2] rule 2: JIKA kelembaban soggy AND suhu cold THEN normal
[3] rule 3: JIKA kelembaban soggy AND suhu cold THEN fast
[4] rule 4: JIKA kelembaban soggy AND suhu normal THEN slow
[5] rule 5: JIKA kelembaban soggy AND suhu normal THEN normal
[6] rule 6: JIKA kelembaban soggy AND suhu normal THEN fast
[7] rule 7: JIKA kelembaban soggy AND suhu warm THEN slow
[8] rule 8: JIKA kelembaban soggy AND suhu warm THEN normal
[9] rule 9: JIKA kelembaban soggy AND suhu warm THEN fast
[10] rule 10: JIKA kelembaban wet AND suhu cold THEN slow
[11] rule 11: JIKA kelembaban wet AND suhu cold THEN normal
[12] rule 12: JIKA kelembaban wet AND suhu cold THEN fast
-
8/19/2019 Tsukatmoto Fuzzy Logic simple example
9/11
[13] rule 13: JIKA kelembaban wet AND suhu normal THEN slow
[14] rule 14: JIKA kelembaban wet AND suhu normal THEN normal
[15] rule 15: JIKA kelembaban wet AND suhu normal THEN fast
[16] rule 16: JIKA kelembaban wet AND suhu warm THEN slow
[17] rule 17: JIKA kelembaban wet AND suhu warm THEN normal
[18] rule 18: JIKA kelembaban wet AND suhu warm THEN fast
[19] rule 19: JIKA kelembaban dry AND suhu cold THEN slow[20] rule 20: JIKA kelembaban dry AND suhu cold THEN normal
[21] rule 21: JIKA kelembaban dry AND suhu cold THEN fast
[22] rule 22: JIKA kelembaban dry AND suhu normal THEN slow
[23] rule 23: JIKA kelembaban dry AND suhu normal THEN normal
[24] rule 24: JIKA kelembaban dry AND suhu normal THEN fast[25] rule 25: JIKA kelembaban dry AND suhu warm THEN slow
[26] rule 26: JIKA kelembaban dry AND suhu warm THEN normal
[27] rule 27 JIKA kelembaban dry AND suhu warm THEN fast
-
8/19/2019 Tsukatmoto Fuzzy Logic simple example
10/11
3. Defuzzyfication
Metode yang digunakan sistem fuzzy ini : metodaTsukamoto (hasil dari peraturan fuzzy berupa himpunanfuzzy)
Pencarian nilai u (α-predikat) dari proses implikasi
menggunakan fungsi MIN α-predikat1 = MIN (μKelembabanSoggy ∩ μSuhuCold)
α-predikat2 = MIN (μKelembabanSoggy ∩ μSuhuNormal)
:
:
dan seterusnya ke-27
-
8/19/2019 Tsukatmoto Fuzzy Logic simple example
11/11
Pencarian Nilai z (hasil inferensi) dari masing-
masing aturan menggunakan :Fungsi linier turun z = (b-(u*(b-a)))
Fungsi linier naik z = (a+u*(b-a))
Dimana : a = batas bawah
b = batas atas
Nilai Crisp Z diperoleh menggunakan metoderata-rata terbobot (Weighted Average) :
Nilai Kecepatan (Z) = .
=
=
